01639nas a2200145   4500008004100000245008200041210006900123300001200192490000900204520115500213100002601368700002701394700002301421856004901444       2017                        eng d00aDeliberative Self-Organizing Traffic Lights with Elementary Cellular Automata0 aDeliberative SelfOrganizing Traffic Lights with Elementary Cellu  a76913700 v20173 aSelf-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave) and a similar performance (close to optimal) compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.1 aZapotecatl, Jorge, L.1 aRosenblueth, David, A.1 aGershenson, Carlos  uhttps://doi.org/10.1155/2017/7691370/769137001891nas a2200169   4500008004100000022001400041245008400055210006900139300001600208490000700224520135000231100001801581700002301599700002701622700002701649856004501676       2012                        eng d  a1099-430000aLife as Thermodynamic Evidence of Algorithmic Structure in Natural Environments0 aLife as Thermodynamic Evidence of Algorithmic Structure in Natur  a2173–21910 v143 aIn evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).1 aZenil, Hector1 aGershenson, Carlos1 aMarshall, James, A. R.1 aRosenblueth, David, A.  uhttp://www.mdpi.com/1099-4300/14/11/2173